Today’s tutorial highlights progress to date through an “end to end” workflow using a real world example of (1) getting, (2) manipulating and (3) enriching a hydrofabric.
If you successfully complete this tutorial, you will create the minimal set of data files (and skills to experiment!) needed for the AWI datastream and NGIAB.
This tutorial can be followed from this webpage which has complete discussion and text surrounding the respective code chunks, or, from the companion R script that can be found here.
Before you jump into this, ensure you have your environment set up by installing R as detailed here # Getting Started
# Install -----------------------------------------------------------------
# install.packages("remotes")
# install.packages("powerjoin")
remotes::install_github("NOAA-OWP/hydrofabric")Attach Package
make_map = function(file, pois) {
hf = read_hydrofabric(file)
mapview::mapview(hf$catchments) + hf$flowpaths + pois
}
### ---- Sample outfiles for today ---- ###
fs::dir_create("tutorial")
source <- '/Users/mjohnson/hydrofabric/'
reference_file <- "tutorial/poudre.gpkg"
refactored_file <- "tutorial/refactored.gpkg"
aggregated_file <- "tutorial/aggregated.gpkg"
nextgen_file <- "tutorial/poudre_ng.gpkg"
model_atts_file <- "tutorial/poudre_ng_attributes.parquet"
model_weights_file <- "tutorial/poudre_ng_weights.parquet"Get Reference Fabric (subsetting)
For this example, we will use NWIS 06752260 that sits on
the Cache La Poudre River in Fort Collins, Colorado. You can use any
USGS gage you desire, or any of the 167,948 found via lynker-spatial
hydrolocation inventory.
The lynker-spatial hydrolocation inventory is both a subset and superset of the community POI set. Meaning, we use a subset of the community POIs, and add a selection needed for NextGen modeling. This include (but are not limited to) the NWS LIDs, Coastal/Terristrail instactions, NWM reservoirs and lakes, Coastal Gages, and more!
## --- Define starting feature by source and ID
## https://waterdata.usgs.gov/monitoring-location/06752260
## https://reference.geoconnex.us/collections/gages/items?provider_id=06752260
# Use get_subset to build a reference subset
get_subset(
hl_uri = "Gages-06752260",
source = using_local_example,
type = "reference",
hf_version = "2.2",
lyrs = c("divides", "flowlines", "network"),
outfile = reference_file,
overwrite = TRUE
)
st_layers(reference_file)## Driver: GPKG
## Available layers:
## layer_name geometry_type features fields crs_name
## 1 divides Polygon 1122 5 NAD83 / Conus Albers
## 2 flowlines Line String 1129 19 NAD83 / Conus Albers
## 3 network NA 1145 23 <NA>Get some Points of Interest
hf = read_hydrofabric(reference_file)
pois = open_dataset(glue("{source}/v2.2/conus_hl")) %>%
filter(hl_source == 'GFv20',
vpuid %in% unique(hf$flowpaths$vpuid),
hf_id %in% hf$flowpaths$id) %>%
collect() %>%
st_as_sf(coords = c("X", "Y"), crs = 5070)
make_map(reference_file, pois)Build a Refactored Fabric
refactored = refactor(
reference_file,
split_flines_meters = 10000,
collapse_flines_meters = 1000,
collapse_flines_main_meters = 1000,
pois = pois,
fac = '/vsis3/lynker-spatial/gridded-resources/fac.vrt',
fdr = '/vsis3/lynker-spatial/gridded-resources/fdr.vrt',
outfile = refactored_file
)
make_map(refactored_file, pois)Build an Aggregated Network
hydrolocations = read_sf(refactored_file, 'lookup_table') %>%
inner_join(pois, by = c("NHDPlusV2_COMID" = "hf_id")) %>%
select(poi_id, NHDPlusV2_COMID, id = reconciled_ID) %>%
distinct()
head(hydrolocations)## # A tibble: 6 × 3
## poi_id NHDPlusV2_COMID id
## <int> <dbl> <int>
## 1 37345 2899997 2
## 2 37014 2899553 10
## 3 36913 2900669 15
## 4 36920 2900581 24
## 5 36914 2900571 28
## 6 36664 2898115 44
aggregate_to_distribution(
gpkg = refactored_file,
hydrolocations = hydrolocations,
ideal_size_sqkm = 10,
min_length_km = 1,
min_area_sqkm = 3,
outfile = aggregated_file,
overwrite = TRUE )
make_map(aggregated_file, pois)Generate a NextGen Network
unlink(nextgen_file)
apply_nexus_topology(aggregated_file, export_gpkg = nextgen_file)## [1] "tutorial/poudre_ng.gpkg"
hf = read_hydrofabric(nextgen_file)
make_map(nextgen_file, read_sf(nextgen_file, "nexus"))Populate Data Needed for CFE/NOM/PET
vsi <- "/vsis3/lynker-spatial/gridded-resources"
div <- read_sf(nextgen_file, "divides")X Y (for forcing downscaling)
d1 <- st_centroid(div) |>
st_transform(4326) |>
st_coordinates() |>
data.frame() |>
mutate(divide_id = div$divide_id)Elevation data for Forcing downscaling and NOAH-OWP
dem_vars <- c("elev", "slope", "aspect")
r <- rast(glue('{vsi}/250m_grids/usgs_250m_{dem_vars}.tif'))
d2 <- execute_zonal(r[[1:2]],
div, ID = "divide_id",
join = FALSE) |>
setNames(c("divide_id", "elevation_mean", " slope"))
d3 <- execute_zonal(r[[3]],
div, ID = "divide_id", fun = circular_mean,
join = FALSE) |>
setNames(c("divide_id", "aspect_c_mean"))GW Routing parameters
crosswalk <- as_sqlite(nextgen_file, "network") |>
select(hf_id, divide_id) |>
collect()
d5 <- open_dataset(glue("{source}/v2.2/reference/conus_routelink")) |>
select(hf_id , starts_with("gw_")) |>
inner_join(mutate(crosswalk, hf_id = as.integer(hf_id)), by = "hf_id") |>
group_by(divide_id) |>
collect() |>
summarize(
gw_Coeff = round(weighted.mean(gw_Coeff, w = gw_Area_sqkm, na.rm = TRUE), 9),
gw_Zmax_mm = round(weighted.mean(gw_Zmax_mm, w = gw_Area_sqkm, na.rm = TRUE), 9),
gw_Expon = mode(floor(gw_Expon))
)
model_attributes <- power_full_join(list(d1, d2, d3, d4, d5), by = "divide_id")
write_parquet(model_attributes, model_atts_file)Weight Grids
type = "medium_range.forcing"
w = weight_grid(rast(glue('{vsi}/{type}.tif')), div, ID = "divide_id") |>
mutate(grid_id = type)
head(w)
write_parquet(w, model_weights_file)oh BTW area was been calculated in Rl_ls
Extacting Cross Sections
hyperlink to bew 3D-vignette link to JOSS paper
crosswalk <- as_sqlite(nextgen_file, "network") |>
select(hf_id, id, divide_id, hydroseq, poi_id) |>
filter(!is.na(poi_id)) %>%
collect() %>%
slice_min(hydroseq)
open_dataset(glue("{source}/v2.2/reference/conus_routelink/")) |>
select(hf_id, starts_with("ml_")) ## FileSystemDataset (query)
## hf_id: int32
## ml_tw_inchan_m: double
## ml_tw_bf_m: double
## ml_y_inchan_m: double
## ml_y_bf_m: double
## ml_ahg_c: double
## ml_ahg_f: double
## ml_ahg_a: double
## ml_ahg_b: double
## ml_ahg_k: double
## ml_ahg_m: double
## ml_r: double
## ml_bf_channel_area_m2: double
## ml_inchan_channel_area_m2: double
## ml_bf_channel_perimeter_m: double
## ml_inchan_channel_perimeter_m: double
## ml_roughness: double
## ml_hf_source: string
##
## See $.data for the source Arrow object
(cs <- open_dataset(glue("{source}/v2.2/reference/conus_routelink/")) |>
select(hf_id, ml_y_bf_m, ml_tw_bf_m, ml_r) %>%
inner_join(mutate(crosswalk, hf_id = as.integer(hf_id)), by = "hf_id") |>
collect() %>%
summarise(TW = mean(ml_tw_bf_m),
r = mean(ml_r),
Y = mean(ml_y_bf_m),
poi_id = poi_id[1]))## # A tibble: 1 × 4
## TW r Y poi_id
## <dbl> <dbl> <dbl> <chr>
## 1 19.6 36.3 1.48 35836
bathy = AHGestimation::cross_section(r = cs$r, TW = cs$TW, Ymax = cs$Y)
plot(bathy$x, bathy$Y, type = "l",
ylab = "Releative distance (m)",
xlab = "Depth (m)",
main = glue("Average XS at POI: {cs$poi_id}"))
Populate Flowpath Attributes
add_flowpath_attributes(nextgen_file, source = source)## [1] "tutorial/poudre_ng.gpkg"## # A tibble: 6 × 13
## fid id rl_Qi_m3s rl_MusX rl_n rl_So rl_ChSlp rl_BtmWdth_m
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 wb-1 0 0.2 0.06 0.0197 0.517 3.91
## 2 2 wb-10 0 0.2 0.0565 0.0479 0.420 9.15
## 3 3 wb-100 0 0.2 0.06 0.0971 0.641 2.33
## 4 4 wb-101 0 0.2 0.06 0.064 0.634 2.39
## 5 5 wb-102 0 0.2 0.06 0.0726 0.628 2.45
## 6 6 wb-103 0 0.2 0.06 0.0553 0.679 2.05
## # ℹ 5 more variables: rl_Kchan_mmhr <dbl>, rl_nCC <dbl>, rl_TopWdthCC_m <dbl>,
## # rl_TopWdth_m <dbl>, length_m <dbl>Adding GPKG Symbology
append_style(nextgen_file, layer_names = c("divides", "flowpaths", "nexus"))## [1] "tutorial/poudre_ng.gpkg"
'qgis -p geopackage:tutorial/poudre_ng.gpkg?projectName=DevCon' %>% system()qgis -p geopackage:tutorial/poudre_ng.gpkg?projectName=DevCon